---
title: Google ADK
description: "Track and analyze your Google Agent Development Kit (ADK) AI agents with AgentOps"
---

AgentOps provides seamless integration with [Google Agent Development Kit (ADK)](https://google.github.io/adk-docs/), allowing you to track and analyze all your ADK agent interactions automatically.

## Installation

<CodeGroup>
```bash pip
pip install agentops google-adk
```
```bash poetry
poetry add agentops google-adk
```
```bash uv
uv pip install agentops google-adk
```
</CodeGroup>

## Setting Up API Keys

Before using Google ADK with AgentOps, you need to set up your API keys. You can obtain:
- **GOOGLE_API_KEY**: From the [Google AI Studio](https://aistudio.google.com/app/apikey)
- **AGENTOPS_API_KEY**: From your [AgentOps Dashboard](https://app.agentops.ai/)

Then to set them up, you can either export them as environment variables or set them in a `.env` file.

<CodeGroup>
```bash Export to CLI
export GOOGLE_API_KEY="your_google_api_key_here"
export AGENTOPS_API_KEY="your_agentops_api_key_here"
```
```txt Set in .env file
GOOGLE_API_KEY="your_google_api_key_here"
AGENTOPS_API_KEY="your_agentops_api_key_here"
```
</CodeGroup>

Then load the environment variables in your Python code:

```python
from dotenv import load_dotenv
import os

# Load environment variables from .env file
load_dotenv()

# Set up environment variables with fallback values
os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")
```

## Usage

Initialize AgentOps at the beginning of your application to automatically track all Google ADK agent interactions:

```python
import asyncio
import json
from pydantic import BaseModel, Field

import agentops
from google.adk.agents import LlmAgent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types

agentops.init()

# --- 1. Define Constants ---
APP_NAME = "agent_comparison_app"
USER_ID = "test_user_456"
SESSION_ID_TOOL_AGENT = "session_tool_agent_xyz"
SESSION_ID_SCHEMA_AGENT = "session_schema_agent_xyz"
MODEL_NAME = "gemini-2.0-flash"

# --- 2. Define Schemas ---

# Input schema used by both agents
class CountryInput(BaseModel):
    country: str = Field(description="The country to get information about.")

# Output schema ONLY for the second agent
class CapitalInfoOutput(BaseModel):
    capital: str = Field(description="The capital city of the country.")
    # Note: Population is illustrative; the LLM will infer or estimate this
    # as it cannot use tools when output_schema is set.
    population_estimate: str = Field(description="An estimated population of the capital city.")

# --- 3. Define the Tool (Only for the first agent) ---
def get_capital_city(country: str) -> str:
    """Retrieves the capital city of a given country."""
    print(f"\n-- Tool Call: get_capital_city(country='{country}') --")
    country_capitals = {
        "united states": "Washington, D.C.",
        "canada": "Ottawa",
        "france": "Paris",
        "japan": "Tokyo",
    }
    result = country_capitals.get(country.lower(), f"Sorry, I couldn't find the capital for {country}.")
    print(f"-- Tool Result: '{result}' --")
    return result

# --- 4. Configure Agents ---

# Agent 1: Uses a tool and output_key
capital_agent_with_tool = LlmAgent(
    model=MODEL_NAME,
    name="capital_agent_tool",
    description="Retrieves the capital city using a specific tool.",
    instruction="""You are a helpful agent that provides the capital city of a country using a tool.
The user will provide the country name in a JSON format like {"country": "country_name"}.
1. Extract the country name.
2. Use the `get_capital_city` tool to find the capital.
3. Respond clearly to the user, stating the capital city found by the tool.
""",
    tools=[get_capital_city],
    input_schema=CountryInput,
    output_key="capital_tool_result", # Store final text response
)

# Agent 2: Uses output_schema (NO tools possible)
structured_info_agent_schema = LlmAgent(
    model=MODEL_NAME,
    name="structured_info_agent_schema",
    description="Provides capital and estimated population in a specific JSON format.",
    instruction=f"""You are an agent that provides country information.
The user will provide the country name in a JSON format like {{"country": "country_name"}}.
Respond ONLY with a JSON object matching this exact schema:
{json.dumps(CapitalInfoOutput.model_json_schema(), indent=2)}
Use your knowledge to determine the capital and estimate the population. Do not use any tools.
""",
    # *** NO tools parameter here - using output_schema prevents tool use ***
    input_schema=CountryInput,
    output_schema=CapitalInfoOutput, # Enforce JSON output structure
    output_key="structured_info_result", # Store final JSON response
)

# --- 5. Set up Session Management and Runners ---
session_service = InMemorySessionService()

# Create a runner for EACH agent
capital_runner = Runner(
    agent=capital_agent_with_tool,
    app_name=APP_NAME,
    session_service=session_service
)
structured_runner = Runner(
    agent=structured_info_agent_schema,
    app_name=APP_NAME,
    session_service=session_service
)

# --- 6. Define Agent Interaction Logic ---
async def call_agent_and_print(
    runner_instance: Runner,
    agent_instance: LlmAgent,
    session_id: str,
    query_json: str
):
    """Sends a query to the specified agent/runner and prints results."""
    print(f"\n>>> Calling Agent: '{agent_instance.name}' | Query: {query_json}")

    user_content = types.Content(role='user', parts=[types.Part(text=query_json)])

    final_response_content = "No final response received."
    async for event in runner_instance.run_async(user_id=USER_ID, session_id=session_id, new_message=user_content):
        # print(f"Event: {event.type}, Author: {event.author}") # Uncomment for detailed logging
        if event.is_final_response() and event.content and event.content.parts:
            # For output_schema, the content is the JSON string itself
            final_response_content = event.content.parts[0].text

    print(f"<<< Agent '{agent_instance.name}' Response: {final_response_content}")

    current_session = await session_service.get_session(app_name=APP_NAME,
                                                         user_id=USER_ID,
                                                         session_id=session_id)
    stored_output = current_session.state.get(agent_instance.output_key)

    # Pretty print if the stored output looks like JSON (likely from output_schema)
    print(f"--- Session State ['{agent_instance.output_key}']: ", end="")
    try:
        # Attempt to parse and pretty print if it's JSON
        parsed_output = json.loads(stored_output)
        print(json.dumps(parsed_output, indent=2))
    except (json.JSONDecodeError, TypeError):
         # Otherwise, print as string
        print(stored_output)
    print("-" * 30)


# --- 7. Run Interactions ---
async def main():
# Create sessions
    await session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID_TOOL_AGENT)
    await session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID_SCHEMA_AGENT)
    
    print("--- Testing Agent with Tool ---")
    await call_agent_and_print(capital_runner, capital_agent_with_tool, SESSION_ID_TOOL_AGENT, '{"country": "France"}')

    print("\n\n--- Testing Agent with Output Schema (No Tool Use) ---")
    await call_agent_and_print(structured_runner, structured_info_agent_schema, SESSION_ID_SCHEMA_AGENT, '{"country": "Japan"}')

asyncio.run(main())
```

## Examples

<CardGroup cols={2}>
  <Card title="Human Approval Example" icon="notebook" href="/v2/examples/google_adk" newTab={true}>
    Implement human-in-the-loop approval workflows with Google ADK agents
  </Card>
</CardGroup>

Visit your [AgentOps Dashboard](https://app.agentops.ai) to see detailed traces of your Google ADK agent interactions, tool usage, and session management.

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